Fuzzy Image Enhancement: An Overview. In: Kerre EE, Nachtegael M, editors. Fuzzy Techniques in Image Processing. Heidelberg: Physica-Verlag HD; 2000. pp. 137-71. [DOI: 10.1007/978-3-7908-1847-5_5]

Mammogram Enhancement Using Intuitionistic Fuzzy Sets

OBJECTIVE

Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets.

METHODS

The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel.

RESULTS

This algorithm can improve the contrast and visual quality of regions of interest.

CONCLUSION

Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods.

SIGNIFICANCE

This algorithm has potential for understanding and determining abnormalities.

Feature-Linking Model for Image Enhancement

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.

A coarse-to-fine approach to prostate boundary segmentation in ultrasound images

Background

In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented.

Methods

A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation.

Results

A total average similarity of 98.76%(± 0.68) with gold standards was achieved.

Conclusion

The proposed approach represents a robust and accurate approach to prostate segmentation.